Collective aspects of privacy in the Twitter social network
Preserving individual control over private information is one of the rising concerns in our digital society. Online social networks exist in application ecosystems that allow them to access data from other services, for example gathering contact lists through mobile phone applications. Such data access might allow social networking sites to create shadow profiles with information about non-users that has been inferred from information shared by the users of the social network. This possibility motivates the shadow profile hypothesis: the data shared by the users of an online service predicts personal information of non-users of the service.
We test this hypothesis for the first time on Twitter, constructing a dataset of users that includes profile biographical text, location information, and bidirectional friendship links. We evaluate the predictability of the location of a user by using only information given by friends of the user that joined Twitter before the user did. This way, we audit the historical prediction power of Twitter data for users that had not joined Twitter yet.
Our results indicate that information shared by users in Twitter can be predictive of the location of individuals outside Twitter. Furthermore, we observe that the quality of this prediction increases with the tendency of Twitter users to share their mobile phone contacts and is more accurate for individuals with more contacts inside Twitter. We further explore the predictability of biographical information of non-users, finding evidence in line with our results for locations. These findings illustrate that individuals are not in full control of their online privacy and that sharing personal data with a social networking site is a decision that is collectively mediated by the decisions of others.
D. Garcia, M. Goel, A. K. Agrawal, P. Kumaragurum, Collective aspects of privacy in the Twitter social network, EPJ Data Science 7 (3) (2018)